CONF farrahi:acmmm:2008/IDIAP What Did You Do Today? Discovering Daily Routines from Large-Scale Mobile Data Farrahi, Katayoun Gatica-Perez, Daniel EXTERNAL http://publications.idiap.ch/attachments/papers/2008/farrahi-acmmm-2008.pdf PUBLIC http://publications.idiap.ch/index.php/publications/showcite/farrahi:rr08-49 Related documents ACM International Conference on Multimedia (ACMMM) 2008 IDIAP-RR 08-49 We present a framework built from two Hierarchical Bayesian topic models to discover human location-driven routines from mobile phones. The framework uses location-driven bag representations of people's daily activities obtained from celltower connections. Using 68 000+ hours of real-life human data from the Reality Mining dataset, we successfully discover various types of routines. The first studied model, Latent Dirichlet Allocation (LDA,',','), automatically discovers characteristic routines for all individuals in the study, including ``going to work at 10am", ``leaving work at night", or ``staying home for the entire evening". In contrast, the second methodology with the Author Topic model (ATM) finds routines characteristic of a selected groups of users, such as ``being at home in the mornings and evenings while being out in the afternoon", and ranks users by their probability of conforming to certain daily routines. REPORT farrahi:rr08-49/IDIAP What Did You Do Today? Discovering Daily Routines from Large-Scale Mobile Data Farrahi, Katayoun Gatica-Perez, Daniel EXTERNAL http://publications.idiap.ch/attachments/reports/2008/farrahi-idiap-rr-08-49.pdf PUBLIC Idiap-RR-49-2008 2008 IDIAP To appear in ACMMM08 We present a framework built from two Hierarchical Bayesian topic models to discover human location-driven routines from mobile phones. The framework uses location-driven bag representations of people's daily activities obtained from celltower connections. Using 68 000+ hours of real-life human data from the Reality Mining dataset, we successfully discover various types of routines. The first studied model, Latent Dirichlet Allocation (LDA,',','), automatically discovers characteristic routines for all individuals in the study, including ``going to work at 10am", ``leaving work at night", or ``staying home for the entire evening". In contrast, the second methodology with the Author Topic model (ATM) finds routines characteristic of a selected groups of users, such as ``being at home in the mornings and evenings while being out in the afternoon", and ranks users by their probability of conforming to certain daily routines.